The objectives of biometric techniques are to identify living beings. These techniques can be used in the context of applications requiring a certain security level, such as, for example, controlled access to sensitive sites.
For this, a morphological analysis applied to the individuals is implemented in order to identify the physical characteristics that are specific to them. This analysis is based, for example, on the iris or the fingerprints.
When it comes to analyzing the iris, an exemplary existing analysis method is the so-called Daugman method, described in the U.S. Pat. No. 5,291,560. This method allows comparison between several iris-representative digital samples and makes it possible to then determine whether these samples correspond to one and the same individual. For this, the aim of a first step is to segment and normalize the irises and then there is a step which aims to extract therefrom a binary code to compare it to a reference. The extraction of the binary code is done by applying a phase demodulation around points of application to transform the texture of the iris into a binary code.
During the segmentation implemented for the analysis methods, the outlines of the iris are usually considered to be circular. Thus, circles delimiting the outlines of the iris are sought. This search for outlines is a critical processing operation in the recognition process because an error of a few pixels on the estimation of the center or of the radius of the circles significantly reduces the performance levels of the system and degrades the reliability of the recognition. Two methods are usually used to find these circles.
In the context of the Daugman method, an integro-differential circle detection operator is used. This operator depends on three parameters (xc,yc,r) corresponding to the coordinates of the center of the circle and to its radius, and is evaluated over the entire digital image of the eye for a significant range of radii. Following the application of the operator to the image, two sets of three parameters (xc,yc,r) are retained, these two sets corresponding respectively to the inside and outside outlines of the iris.
The main drawback with this method is that the integro-differential operator must be calculated over a significant portion of the image and on a discrete grid limiting the accuracy that can be achieved for the description of the outline. Furthermore, this operator is sensitive to local minima, said minima being introduced for example by artifacts such as portions of eyelid.
A second method for finding the circles delimiting the iris is the Wildes method. Firstly, a filter is applied to the digital image of the eye to implement a detection of edges. For this, a Canny filter can be used. A circular Hough transform is then applied to the image resulting from the detection of edges to find the circles from the detected edges. The main drawback with this method is that it depends strongly on the edge detection method used. Furthermore, the circular Hough transform is an operation that is very costly in terms of computation time.
These two outline detection methods have also been extended for more complex outline types than circles to be able to take into account, for example, outlines of elliptical form. However, in the case of ellipses, the Daugman operator and the elliptical Hough transform significantly increase the computation complexity. Thus, the least squares method is usually used in practice because it is less costly in terms of computation power, even though it is less robust.